Shape and Motion Estimation from Geometric Primitives and Parametric Modelling

This paper presents a new approach to shape and motion estimation based on geometric primitives and relations in a model-based framework. A description of a scene in terms of structured geometric elements sharing relationships allows to derive a parametric model with Euclidian constraints, and a camera model is also proposed to reduce the problem dimensionality. It leads to a sequential MAP estimation, that gives accurate and comprehensible results on real images.

[1]  Rajiv Gupta,et al.  Stereo from uncalibrated cameras , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Reinhard Koch,et al.  Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[3]  Kenichi Kanatani,et al.  Statistical optimization for geometric computation - theory and practice , 1996, Machine intelligence and pattern recognition.

[4]  Pierre-Louis Bazin.  Parametric scene reduction algorithm from geometric relations , 2000, SPIE Optics + Photonics.

[5]  Hans-Hellmut Nagel,et al.  Model-based object tracking in monocular image sequences of road traffic scenes , 1993, International Journal of Computer 11263on.

[6]  William H. Press,et al.  Numerical recipes in C , 2002 .

[7]  René Caubet,et al.  Modelling with constraints: a bibliographical survey , 1998, Proceedings. 1998 IEEE Conference on Information Visualization. An International Conference on Computer Visualization and Graphics (Cat. No.98TB100246).

[8]  André Gagalowicz,et al.  Three dimensional model-based tracking using texture learning and matching , 2000, Pattern Recognit. Lett..

[9]  Takeo Kanade,et al.  A Paraperspective Factorization Method for Shape and Motion Recovery , 1994, IEEE Trans. Pattern Anal. Mach. Intell..